26IM

Technical details

Show code

library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

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kable(gpr) %>% scroll_box(width = "100%")

gdl_id	keep	crop_start	crop_end	thr_dur	extent_N	extent_W	extent_S	extent_E	map_scale	map_max_sample	map_margin	prob_map_s	prob_map_s_calib	prob_map_thr	shift_k	kernel_adjust	calib_lon	calib_lat	calib_1_start	calib_1_end	calib_2_start	calib_2_end	calib_2_lon	calib_2_lat	prob_light_w	thr_prob_percentile	thr_gs	thr_as	RingNo	scientific_name	common_name	mass	wing_span	Color	sta_id_winter
26IM	9	2020-07-14	2021-05-28	0	51	-18	5	16	4	300	30	1	1.3	0.9	0	1.4	8.705503	46.55076	2020-07-14	2020-09-17	2021-05-03	2021-05-28	NA	NA	0.09	0.95	120	100	N643042	Oenanthe oenanthe	Northern wheatear	NA	NA	#636EFA	16

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.

Show code

pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutlier | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  # geom_point(data = subset(pam$pressure, isoutlier), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Pressure calibration

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pressure_ts_bind <- do.call("rbind", shortest_path_timeserie) %>%
  filter(!is.na(sta_id))

pam$pressure %>%
  left_join(pressure_ts_bind %>% dplyr::select(c("date", "pressure0")), by = "date") %>%
  mutate(diff = ifelse(is.na(pressure0), 0, obs - pressure0)) %>%
  filter(sta_id > 0 & !isoutlier) %>%
  group_by(sta_id) %>%
  mutate(sta_id = paste0(sta_id, " (SD=", round(sd(diff), 2), " ; N=", n(), ")")) %>%
  ggplot(aes(x = diff)) +
  geom_histogram(aes(y = (..count..) / tapply(..count.., ..PANEL.., sum)[..PANEL..]), binwidth = .2) +
  facet_wrap(~sta_id) +
  scale_x_continuous(name = "Pressure Geolocator - best match ERA5 (hPa)") +
  scale_y_continuous(name = "Normalized histogram")

Light

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raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
  offset = 0, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

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hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

Show code

 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

  par(mfrow = c(2, 3))
  for (i_s in long_id){
    twl_fl <- twl %>%
      filter(!deleted) %>%
      filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
    sun <-  solar(twl_fl$twilight)
    z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
    hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
    lines(fit_z, col = "red")
    xlab("Zenith angle")
  }

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

Show code

  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
    if (nrow(twl_fl)>0){
    tsimageDeploymentLines(twl_fl$twilight,
                           lon = ts$lon[1], ts$lat[1],
                           offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
      
    }
  }

Stationay period information

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read_csv(paste0("../reports/figure_print/table_transition/transition_", params$gdl_id, ".csv")) %>% kable() %>% scroll_box(width = "100%")

…1	sta_id_s	sta_id_t	flight_duration	as_m	as_s	gs_m	gs_s	ws_m	ws_s	dist_m	dist_s	ws_m_support	ws_m_drift	start_flight	end_flight	sunrise	sunset	as_sp	gs_sp	ws_sp	dist_sp	ws_sp_support	ws_sp_drift	alt_min	alt_max	alt_mean	alt_med	alt_sumdabsdiff	alt_sumposdiff	gdl_id
1	1	2	8.0	38.44546	9.804338	48.75645	11.371858	11.381807	4.5896903	390.05162	90.97487	10.5492088	4.273140	2020-09-16 18:00:00	2020-09-17 02:00:00	2020-09-17 04:57:02	2020-09-16 18:04:51	41.76022	55.93640	16.881902	447.49120	14.9273423	7.8849904	1326.746467	3692.9111	2564.9621	2572.3228	6009.8034	2670.5919	26IM
2	2	3	3.0	34.26814	16.051918	32.57591	16.572495	9.205904	2.7622863	97.72772	49.71748	-0.4354046	9.195602	2020-09-17 18:30:00	2020-09-17 21:30:00	2020-09-18 04:59:50	2020-09-17 18:19:48	33.32318	28.57082	9.712767	85.71247	-3.4966542	9.0615259	385.990928	4120.3701	2709.4552	3248.9909	6424.8419	2690.4627	26IM
3	3	4	0.5	32.71884	21.291929	34.88579	26.650072	10.317075	4.3466711	17.44289	13.32504	3.6252221	9.659183	2020-09-18 20:30:00	2020-09-18 21:00:00	2020-09-19 05:01:59	2020-09-18 18:18:19	30.15767	40.35480	10.498521	20.17740	10.2744155	2.1576228	265.884562	857.0156	561.4501	561.4501	591.1310	0.0000	26IM
4	4	5	1.0	31.02274	14.785335	29.03919	21.830757	20.188789	10.3419456	29.03919	21.83076	4.9665877	19.568347	2020-09-19 21:00:00	2020-09-19 22:00:00	2020-09-20 05:05:18	2020-09-19 18:17:27	30.20256	49.05843	19.093516	49.05843	18.9477866	2.3545121	47.869772	360.1404	200.9374	194.8020	312.2706	0.0000	26IM
5	5	6	9.0	47.30193	20.423458	41.67277	19.826434	8.500212	4.0507183	375.05490	178.43791	-5.1424441	6.768225	2020-09-21 18:30:00	2020-09-22 03:30:00	2020-09-22 05:17:40	2020-09-21 18:15:36	34.41825	32.14229	5.790611	289.28062	-1.8349279	5.4921957	48.730991	2092.5218	1004.0967	832.2751	6471.3157	2843.8858	26IM
6	6	7	8.0	48.44992	23.170336	39.64315	24.563565	14.537876	5.3427690	317.14518	196.50852	-7.1193348	12.675366	2020-09-22 18:30:00	2020-09-23 02:30:00	2020-09-23 05:25:29	2020-09-22 18:21:43	37.66855	29.02755	14.340741	232.22038	-6.3846980	12.8410471	478.831709	2430.8651	1128.6348	1026.7389	4800.9803	2482.7667	26IM
7	7	8	0.5	33.75167	16.003071	28.96387	33.806102	29.370808	6.7105288	14.48194	16.90305	9.7082218	27.719935	2020-09-23 19:00:00	2020-09-23 19:30:00	2020-09-24 05:26:26	2020-09-23 18:25:15	30.82228	0.00000	30.822281	0.00000	NA	NA	443.833072	2417.1014	1430.4672	1430.4672	1973.2683	0.0000	26IM
8	8	9	9.0	41.97030	12.164199	64.93564	18.105592	31.095416	10.7557069	584.42074	162.95033	26.3496003	16.511313	2020-09-25 18:30:00	2020-09-26 03:30:00	2020-09-26 05:21:23	2020-09-25 18:21:58	32.29474	55.96868	24.788329	503.71814	24.1564082	5.5614047	4.162741	2646.0933	1024.5108	1066.9558	7424.9011	4324.1802	26IM
9	9	10	0.5	31.90757	14.961364	45.93178	23.596933	36.215451	4.8770883	22.96589	11.79847	26.1604760	25.043729	2020-09-26 21:00:00	2020-09-26 21:30:00	2020-09-27 05:21:09	2020-09-26 18:11:23	16.83249	45.43486	34.740001	22.71743	32.8807163	11.2127696	1217.415143	1908.9952	1563.2052	1563.2052	691.5801	0.0000	26IM
10	10	11	10.0	46.06701	10.706761	73.34564	9.845241	28.901678	2.4432046	733.45644	98.45241	27.9002346	7.542141	2020-10-05 18:00:00	2020-10-06 04:00:00	2020-10-06 05:32:54	2020-10-05 17:57:39	37.49903	68.01423	31.069805	680.14225	30.7662852	4.3322628	557.576491	1784.4923	1057.2231	935.0307	2663.6428	1093.0468	26IM
11	11	12	12.5	39.71797	12.188840	64.31377	12.823144	25.651547	2.6645073	803.92212	160.28930	25.0082206	5.708834	2020-10-06 18:00:00	2020-10-07 06:30:00	2020-10-07 05:39:40	2020-10-06 18:03:37	35.67554	61.92926	26.381519	774.11579	26.3080328	1.9677309	324.847614	3234.6410	1821.7096	2351.5534	9438.5425	4737.1830	26IM
12	12	13	11.0	42.91235	19.990682	54.79321	19.808003	20.580411	9.3787712	602.72531	217.88803	14.4578080	14.646676	2020-10-07 18:30:00	2020-10-08 05:30:00	2020-10-08 05:35:20	2020-10-07 18:12:22	35.23654	53.31554	26.302849	586.47091	21.5019207	15.1494973	540.328829	3611.0373	2257.1709	2564.8913	10764.9170	5957.2986	26IM
13	13	14	10.0	53.48568	21.467619	49.30647	20.874522	7.179562	2.3711281	493.06472	208.74522	-3.8336170	6.070378	2020-10-08 19:30:00	2020-10-09 05:30:00	2020-10-09 05:22:31	2020-10-08 18:10:14	51.67562	44.23648	7.505599	442.36477	-7.4279184	1.0770530	282.919060	2449.1111	1287.2820	1327.4175	9772.0286	4635.0039	26IM
14	14	15	2.5	31.48530	15.295274	32.57346	17.284253	4.472537	1.2160883	81.43366	43.21063	1.3770445	4.255272	2020-10-10 02:00:00	2020-10-10 04:30:00	2020-10-10 05:19:44	2020-10-09 17:58:33	29.71275	34.14474	4.527460	85.36186	4.4445134	0.8626653	306.722077	432.4263	377.1620	389.8806	238.7801	125.7042	26IM
15	15	16	1.5	26.84548	14.130277	27.08813	16.406363	7.775349	0.9737904	40.63219	24.60954	1.3574712	7.655934	2020-10-10 22:30:00	2020-10-11 00:00:00	2020-10-11 05:17:51	2020-10-10 17:54:48	29.37444	35.87090	7.126289	53.80634	6.6160527	2.6479881	263.765490	425.7458	316.9073	289.0590	321.9771	159.9967	26IM
16	16	17	10.5	30.82062	8.852794	31.67531	14.247368	33.528356	1.3382357	332.59073	149.59736	18.5880631	27.904024	2021-04-01 18:30:00	2021-04-02 05:00:00	2021-04-02 05:32:59	2021-04-01 18:22:18	31.41739	34.47421	33.825683	361.97925	19.5159749	27.6279491	208.015614	3274.7985	1584.3214	1563.2039	9771.3308	4896.1005	26IM
17	17	18	13.0	43.10631	14.982266	47.79141	13.053744	23.473868	3.8061580	621.28830	169.69868	10.2203219	21.132144	2021-04-02 18:30:00	2021-04-03 07:30:00	2021-04-03 05:45:04	2021-04-02 18:35:13	38.38139	44.15370	24.908726	573.99810	12.4209600	21.5908401	222.119740	4566.7993	1755.2097	1639.5668	18690.5892	9365.3884	26IM
18	18	19	21.0	47.52094	17.226328	84.82451	10.675709	45.351775	10.8046636	1781.31460	224.18988	41.2247666	18.902437	2021-04-03 19:00:00	2021-04-04 16:00:00	2021-04-04 04:52:48	2021-04-03 18:52:37	36.40976	82.58790	51.282471	1734.34584	49.1899055	14.4998291	158.821632	3769.9195	2346.4099	2499.0195	18296.7144	8897.6505	26IM
19	19	20	3.0	35.14390	15.974203	29.38300	15.773331	9.440577	4.0589541	88.14899	47.31999	-4.8090535	8.123884	2021-04-05 00:30:00	2021-04-05 03:30:00	2021-04-05 04:50:46	2021-04-04 18:20:34	29.12917	27.72612	8.447643	83.17835	-0.1516371	8.4462822	138.829670	2538.6465	1848.1124	2339.5202	4265.4646	1759.2867	26IM
20	20	21	5.0	39.87572	9.179918	44.16379	10.524302	11.923304	3.2076897	220.81894	52.62151	5.6894192	10.478344	2021-04-06 19:30:00	2021-04-07 00:30:00	2021-04-07 04:43:30	2021-04-06 18:22:49	29.96352	36.03931	13.324628	180.19655	8.0268629	10.6355627	256.791216	1372.4299	756.1969	653.2322	2973.4207	1623.7821	26IM
21	21	22	1.0	30.96389	15.520433	35.36037	16.932450	21.652824	4.0136778	35.36037	16.93245	10.7526912	18.794266	2021-04-07 05:30:00	2021-04-07 06:30:00	2021-04-07 04:41:13	2021-04-06 18:21:24	27.99082	53.40981	25.466712	53.40981	25.4417247	1.1278602	1133.608354	1271.5705	1209.7779	1224.1548	185.3779	137.9622	26IM
22	22	23	1.5	30.50076	13.762845	37.19461	19.705873	32.180960	1.9028052	55.79192	29.55881	20.0130752	25.201012	2021-04-21 18:30:00	2021-04-21 20:00:00	2021-04-22 04:20:14	2021-04-21 18:32:34	27.95237	36.98249	32.486246	55.47373	22.1959819	23.7211833	1235.174838	2586.3149	1848.6267	1786.5085	2605.7130	1254.5729	26IM
23	23	24	9.5	86.45337	4.960686	95.72122	4.351167	9.755181	1.9773638	909.35156	41.33608	9.3162719	2.893204	2021-04-24 19:00:00	2021-04-25 04:30:00	2021-04-25 04:06:12	2021-04-24 18:36:09	77.11822	88.69483	11.588936	842.60093	11.5782200	0.4982484	163.578988	3319.0815	1397.7146	1173.4355	6247.9391	2198.8259	26IM
24	24	25	3.0	31.83508	20.042877	32.34688	20.381341	4.394927	2.1657492	97.04064	61.14402	0.8063113	4.320329	2021-04-25 09:00:00	2021-04-25 12:00:00	2021-04-25 04:00:39	2021-04-24 18:58:16	40.51437	42.17435	1.969965	126.52305	1.6733223	1.0395934	-49.358714	568.9780	287.1836	284.7488	831.7452	213.4085	26IM
25	25	26	6.0	38.70119	11.577197	39.63352	11.369465	2.606847	1.9835443	237.80112	68.21679	1.0070928	2.404458	2021-04-30 19:00:00	2021-05-01 01:00:00	2021-05-01 03:43:28	2021-04-30 19:05:43	31.08300	29.48551	1.974277	176.91308	-1.5746644	1.1908821	286.626717	1203.9795	645.3263	644.8759	3275.3127	1725.0887	26IM
26	26	27	5.0	38.00505	12.923229	30.29100	12.271300	10.226765	2.4928186	151.45500	61.35650	-6.9699387	7.483761	2021-05-02 19:30:00	2021-05-03 00:30:00	2021-05-03 03:33:52	2021-05-02 19:10:04	28.43607	25.07181	7.804989	125.35903	-2.3751147	7.4348289	154.292651	2419.7559	1580.8931	1740.0417	6642.2029	4246.2442	26IM